Quadrotor aerial vehicles, also known as quadcopters, have been in use since the 1920's but were generally disregarded due to poor lifting performance and poor stability. In recent years, quadrotors have experienced a resurgence due to improvements in materials including high strength, light weight composites and improved battery technology.
Application of quadrotors to seemingly simple tasks such as transporting materials in a sling can be limited by instabilities caused by wind, wind gusts, and the acceleration of the quadrotor. In similar applications, such as helicopter lifts, a human pilot can manually counter the effects of a swinging payload. However, even in a piloted vehicle controlling a swinging payload is not a simple task. The challenge is increased for an automatically piloted aerial vehicle when such visual and tactile inputs may not be available. Oscillatory motion in a payload can quickly develop into a situation where the quadrotor's efficiency is reduced or may even become uncontrollable.
Attempts to predict and prevent slung payload oscillation have used a variety of techniques including predicting flight characteristics of the payload and feed-forward only predictions of trajectory of the payload based on a flight path of the lift vehicle. However, these techniques rely on predictive techniques and/or modeling of the load that may not always accurately portray the actual circumstances. Thus, there is a need for a way to quickly identify and correct oscillatory conditions in a slung payload of a quadrotor.